GridSearchCV

yohou.model_selection.search.GridSearchCV

Bases: BaseSearchCV

Exhaustive search over specified parameter values for a forecaster.

Important members are fit, predict, predict_interval, observe, and rewind.

GridSearchCV implements a "fit" method that evaluates all parameter combinations specified in param_grid using time series cross-validation. The parameters of the forecaster are optimized by cross-validated grid-search over a parameter grid.

It also implements "predict", "predict_interval", "observe", "rewind", "observe_predict", and "observe_predict_interval" if the underlying forecaster supports these methods and refit=True.

Parameters

Name	Type	Description	Default
forecaster	BaseForecaster	A forecaster object implementing the yohou forecaster interface with fit and predict methods.	required
param_grid	dict or list of dict	Dictionary with parameter names (str) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. Examples:: param_grid = {"estimator__alpha": [0.1, 1.0, 10.0]} param_grid = [ {"estimator__alpha": [0.1, 1.0], "estimator__l1_ratio": [0.5, 0.7]}, {"estimator__alpha": [10.0, 100.0]}, ]	required
scoring	BaseScorer or dict of {str: BaseScorer}	Strategy to evaluate the performance of the cross-validated model on the test set. If a single BaseScorer instance, the same scorer is used for all folds and stored in cv_results_ with key 'score'. If a dict, keys are scorer names and values are BaseScorer instances. This enables multi-metric evaluation. The refit parameter must be set to a scorer name or False to determine which scorer to use for selecting the best parameters. Unlike sklearn, string scorer names are not supported. You must use yohou.metrics BaseScorer instances (e.g., MeanAbsoluteError(), RootMeanSquaredError()). Examples:: scoring = MeanAbsoluteError() scoring = {"mae": MeanAbsoluteError(), "rmse": RootMeanSquaredError()} Note: For multi-metric evaluation with dict, cv_results_ will contain keys like 'mean_test_mae', 'rank_test_mae', 'mean_test_rmse', etc.	None
n_jobs	int	Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.	None
refit	bool, str, or callable	Refit a forecaster using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a str denoting the scorer that would be used to find the best parameters for refitting the forecaster at the end. Where there are considerations other than maximum score in choosing a best forecaster, refit can be set to a function which returns the selected best_index_ given cv_results_. In that case, the best_forecaster_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available. The refitted forecaster is made available at the best_forecaster_ attribute and permits using predict directly on this GridSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. See scoring parameter to know more about multiple metric evaluation. Examples:: refit = True # Use single scorer or first scorer for multi-metric refit = "mae" # For multi-metric: use 'mae' scorer to select best refit = False # Don't refit, only evaluate refit = lambda cv_results: cv_results["rank_test_mae"].argmin()	True
cv	int, BaseSplitter, or None	Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 5-fold expanding window splitter int, to specify the number of folds in an ExpandingWindowSplitter. An object to be used as a cross-validation generator (must have split and get_n_splits methods). For time series data, typical splitters are: ExpandingWindowSplitter: Train size increases with each fold SlidingWindowSplitter: Fixed train size, sliding forward	None
verbose	int	Controls the verbosity: the higher, the more messages. 0 : the total number of fits is displayed. 1 : the score and time for each fit and parameter candidate is displayed. 2 : the fold indices and scores for each candidate are displayed. 10 : the parameter candidate indices are displayed.	0
pre_dispatch	int or str	Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs An int, giving the exact number of total jobs that are spawned A str, giving an expression as a function of n_jobs, as in '2*n_jobs'	'2*n_jobs'
error_score	'raise' or numeric	Value to assign to the score if an error occurs in forecaster fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.	np.nan
return_train_score	bool	If False, the cv_results_ attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.	False

Attributes

Name	Type	Description
cv_results_	dict of numpy (masked) ndarrays	A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame. For instance the below given table:: +-----------+------------+-------------------+---+-----------------+ |param_alpha|param_gamma |param_degree |...|rank_test_score | +===========+============+===================+===+=================+ | 1.0 | -- | -- |...| 2 | +-----------+------------+-------------------+---+-----------------+ | 10.0 | -- | -- |...| 1 | +-----------+------------+-------------------+---+-----------------+ | 100.0 | -- | -- |...| 3 | +-----------+------------+-------------------+---+-----------------+ | -- | 0.1 | 2 |...| 5 | +-----------+------------+-------------------+---+-----------------+ | -- | 0.2 | 3 |...| 4 | +-----------+------------+-------------------+---+-----------------+ will be represented by a cv_results_ dict of:: { 'param_alpha': masked_array(data=[1.0, 10.0, 100.0, --, --], mask=[False False False True True]...), 'param_gamma': masked_array(data=[--, --, --, 0.1, 0.2], mask=[ True True True False False]...), 'param_degree': masked_array(data=[--, --, --, 2, 3], mask=[ True True True False False]...), 'split0_test_score' : [0.80, 0.70, 0.60, 0.75, 0.85], 'split1_test_score' : [0.82, 0.75, 0.65, 0.72, 0.80], 'mean_test_score' : [0.81, 0.725, 0.625, 0.735, 0.825], 'std_test_score' : [0.01, 0.025, 0.025, 0.015, 0.025], 'rank_test_score' : [2, 3, 5, 4, 1], 'split0_train_score' : [0.85, 0.80, 0.75, 0.82, 0.90], 'split1_train_score' : [0.87, 0.82, 0.77, 0.84, 0.92], 'mean_train_score' : [0.86, 0.81, 0.76, 0.83, 0.91], 'std_train_score' : [0.01, 0.01, 0.01, 0.01, 0.01], 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49, 0.55], 'std_fit_time' : [0.01, 0.02, 0.01, 0.01, 0.02], 'mean_score_time' : [0.01, 0.06, 0.04, 0.04, 0.05], 'std_score_time' : [0.00, 0.00, 0.00, 0.01, 0.00], 'params' : [{'alpha': 1.0}, {'alpha': 10.0}, ...], } NOTE: The key 'params' is used to store a list of parameter settings dicts for all the parameter candidates. The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the cv_results_ dict at the keys ending with that scorer's name ('_<scorer_name>') instead of '_score' shown above. ('split0_test_mae', 'mean_train_rmse' etc.)
best_forecaster_	BaseForecaster	Forecaster that was chosen by the search, i.e. forecaster which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False. See refit parameter for more information on allowed values.
best_score_	float	Mean cross-validated score of the best_forecaster_. Follows sklearn's sign convention: for lower_is_better scorers (e.g. MAE, RMSE) the value is negated so that higher always means better. The raw metric value is -best_score_. For multi-metric evaluation, this is present only if refit is specified. This attribute is not available if refit is a function.
best_params_	dict	Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if refit is specified.
best_index_	int	The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_). For multi-metric evaluation, this is present only if refit is specified.
scorer_	BaseScorer or dict	Scorer function(s) used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
n_splits_	int	The number of cross-validation splits (folds/iterations).
refit_time_	float	Seconds used for refitting the best forecaster on the whole dataset. This is present only if refit is not False.
multimetric_	bool	Whether or not the scorers compute several metrics.
n_features_in_	int	Number of features seen during fit. Only defined if best_forecaster_ is defined (see the documentation for the refit parameter for more details) and that best_forecaster_ exposes n_features_in_ when fit.
feature_names_in_	ndarray of shape (n_features_in_,)	Names of features seen during fit. Only defined if best_forecaster_ is defined (see the documentation for the refit parameter for more details) and that best_forecaster_ exposes feature_names_in_ when fit.

See Also

• RandomizedSearchCV : Randomized search over parameter distributions.
• ExpandingWindowSplitter : Cross-validation with expanding training windows.
• SlidingWindowSplitter : Cross-validation with sliding fixed-size windows.
• MeanAbsoluteError : Mean absolute error scorer.
• RootMeanSquaredError : Root mean squared error scorer.

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit scorer is passed in which case it is used instead.

If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

Examples

>>> from yohou.point import PointReductionForecaster
>>> from yohou.model_selection import GridSearchCV
>>> from yohou.metrics import MeanAbsoluteError
>>> import polars as pl
>>> from datetime import datetime, timedelta
>>> # Create sample data
>>> dates = [datetime(2020, 1, 1) + timedelta(days=i) for i in range(100)]
>>> y = pl.DataFrame({"time": dates, "value": range(100)})
>>> # Define parameter grid
>>> param_grid = {
...     "estimator__alpha": [0.1, 1.0, 10.0],
...     "feature_transformer__lags": [[1], [1, 2]],
... }
>>> # Single-metric search
>>> search = GridSearchCV(
...     forecaster=PointReductionForecaster(),
...     param_grid=param_grid,
...     scoring=MeanAbsoluteError(),
...     cv=3,
... )
>>> search.fit(y, forecasting_horizon=5)
>>> search.best_params_
>>> y_pred = search.predict(forecasting_horizon=5)
>>>
>>> # Multi-metric search
>>> from yohou.metrics import RootMeanSquaredError
>>> scoring = {"mae": MeanAbsoluteError(), "rmse": RootMeanSquaredError()}
>>> search = GridSearchCV(
...     forecaster=PointReductionForecaster(),
...     param_grid=param_grid,
...     scoring=scoring,
...     refit="mae",  # Use 'mae' to select best parameters
...     cv=3,
... )
>>> search.fit(y, forecasting_horizon=5)
>>> search.best_params_
>>> # cv_results_ contains both 'mean_test_mae' and 'mean_test_rmse'
>>> search.cv_results_["mean_test_mae"]
>>> search.cv_results_["mean_test_rmse"]

Source Code

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class GridSearchCV(BaseSearchCV):
    """Exhaustive search over specified parameter values for a forecaster.

    Important members are fit, predict, predict_interval, observe, and rewind.

    GridSearchCV implements a "fit" method that evaluates all parameter
    combinations specified in param_grid using time series cross-validation.
    The parameters of the forecaster are optimized by cross-validated
    grid-search over a parameter grid.

    It also implements "predict", "predict_interval", "observe", "rewind",
    "observe_predict", and "observe_predict_interval" if the underlying
    forecaster supports these methods and refit=True.

    Parameters
    ----------
    forecaster : BaseForecaster
        A forecaster object implementing the yohou forecaster interface
        with fit and predict methods.

    param_grid : dict or list of dict
        Dictionary with parameter names (`str`) as keys and lists of
        parameter settings to try as values, or a list of such
        dictionaries, in which case the grids spanned by each dictionary
        in the list are explored. This enables searching over any sequence
        of parameter settings.

        Examples::

            param_grid = {"estimator__alpha": [0.1, 1.0, 10.0]}
            param_grid = [
                {"estimator__alpha": [0.1, 1.0], "estimator__l1_ratio": [0.5, 0.7]},
                {"estimator__alpha": [10.0, 100.0]},
            ]

    scoring : BaseScorer or dict of {str: BaseScorer}
        Strategy to evaluate the performance of the cross-validated model
        on the test set.

        If a single BaseScorer instance, the same scorer is used for all
        folds and stored in cv_results_ with key 'score'.

        If a dict, keys are scorer names and values are BaseScorer instances.
        This enables multi-metric evaluation. The ``refit`` parameter must
        be set to a scorer name or False to determine which scorer to use
        for selecting the best parameters.

        Unlike sklearn, string scorer names are not supported. You must use
        yohou.metrics BaseScorer instances (e.g., MeanAbsoluteError(),
        RootMeanSquaredError()).

        Examples::

            scoring = MeanAbsoluteError()
            scoring = {"mae": MeanAbsoluteError(), "rmse": RootMeanSquaredError()}

        Note: For multi-metric evaluation with dict, cv_results_ will contain
        keys like 'mean_test_mae', 'rank_test_mae', 'mean_test_rmse', etc.

    n_jobs : int, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a ``joblib.parallel_backend`` context.
        ``-1`` means using all processors.

    refit : bool, str, or callable, default=True
        Refit a forecaster using the best found parameters on the whole dataset.

        For multiple metric evaluation, this needs to be a ``str`` denoting the
        scorer that would be used to find the best parameters for refitting
        the forecaster at the end.

        Where there are considerations other than maximum score in
        choosing a best forecaster, ``refit`` can be set to a function which
        returns the selected ``best_index_`` given ``cv_results_``. In that
        case, the ``best_forecaster_`` and ``best_params_`` will be set
        according to the returned ``best_index_`` while the ``best_score_``
        attribute will not be available.

        The refitted forecaster is made available at the ``best_forecaster_``
        attribute and permits using ``predict`` directly on this
        ``GridSearchCV`` instance.

        Also for multiple metric evaluation, the attributes ``best_index_``,
        ``best_score_`` and ``best_params_`` will only be available if
        ``refit`` is set and all of them will be determined w.r.t this specific
        scorer.

        See ``scoring`` parameter to know more about multiple metric
        evaluation.

        Examples::

            refit = True  # Use single scorer or first scorer for multi-metric
            refit = "mae"  # For multi-metric: use 'mae' scorer to select best
            refit = False  # Don't refit, only evaluate
            refit = lambda cv_results: cv_results["rank_test_mae"].argmin()

    cv : int, BaseSplitter, or None, default=None
        Determines the cross-validation splitting strategy.

        Possible inputs for cv are:

        - None, to use the default 5-fold expanding window splitter
        - int, to specify the number of folds in an
          ``ExpandingWindowSplitter``.
        - An object to be used as a cross-validation generator (must have
          ``split`` and ``get_n_splits`` methods).

        For time series data, typical splitters are:

        - ``ExpandingWindowSplitter``: Train size increases with each fold
        - ``SlidingWindowSplitter``: Fixed train size, sliding forward

    verbose : int, default=0
        Controls the verbosity: the higher, the more messages.

        - >0 : the total number of fits is displayed.
        - >1 : the score and time for each fit and parameter candidate is displayed.
        - >2 : the fold indices and scores for each candidate are displayed.
        - >10 : the parameter candidate indices are displayed.

    pre_dispatch : int or str, default='2*n_jobs'
        Controls the number of jobs that get dispatched during parallel
        execution. Reducing this number can be useful to avoid an
        explosion of memory consumption when more jobs get dispatched
        than CPUs can process. This parameter can be:

        - None, in which case all the jobs are immediately
          created and spawned. Use this for lightweight and
          fast-running jobs, to avoid delays due to on-demand
          spawning of the jobs

        - An int, giving the exact number of total jobs that are
          spawned

        - A str, giving an expression as a function of n_jobs,
          as in '2*n_jobs'

    error_score : 'raise' or numeric, default=np.nan
        Value to assign to the score if an error occurs in forecaster fitting.
        If set to 'raise', the error is raised. If a numeric value is given,
        FitFailedWarning is raised. This parameter does not affect the refit
        step, which will always raise the error.

    return_train_score : bool, default=False
        If ``False``, the ``cv_results_`` attribute will not include training
        scores.

        Computing training scores is used to get insights on how different
        parameter settings impact the overfitting/underfitting trade-off.
        However computing the scores on the training set can be computationally
        expensive and is not strictly required to select the parameters that
        yield the best generalization performance.

    Attributes
    ----------
    cv_results_ : dict of numpy (masked) ndarrays
        A dict with keys as column headers and values as columns, that can be
        imported into a pandas ``DataFrame``.

        For instance the below given table::

            +-----------+------------+-------------------+---+-----------------+
            |param_alpha|param_gamma |param_degree       |...|rank_test_score  |
            +===========+============+===================+===+=================+
            |  1.0      |     --     |         --        |...|        2        |
            +-----------+------------+-------------------+---+-----------------+
            |  10.0     |     --     |         --        |...|        1        |
            +-----------+------------+-------------------+---+-----------------+
            |  100.0    |     --     |         --        |...|        3        |
            +-----------+------------+-------------------+---+-----------------+
            |   --      |    0.1     |         2         |...|        5        |
            +-----------+------------+-------------------+---+-----------------+
            |   --      |    0.2     |         3         |...|        4        |
            +-----------+------------+-------------------+---+-----------------+

        will be represented by a cv_results_ dict of::

            {
            'param_alpha': masked_array(data=[1.0, 10.0, 100.0, --, --],
                                        mask=[False False False  True  True]...),
            'param_gamma': masked_array(data=[--, --, --, 0.1, 0.2],
                                       mask=[ True  True  True False False]...),
            'param_degree': masked_array(data=[--, --, --, 2, 3],
                                         mask=[ True  True  True False False]...),
            'split0_test_score'  : [0.80, 0.70, 0.60, 0.75, 0.85],
            'split1_test_score'  : [0.82, 0.75, 0.65, 0.72, 0.80],
            'mean_test_score'    : [0.81, 0.725, 0.625, 0.735, 0.825],
            'std_test_score'     : [0.01, 0.025, 0.025, 0.015, 0.025],
            'rank_test_score'    : [2, 3, 5, 4, 1],
            'split0_train_score' : [0.85, 0.80, 0.75, 0.82, 0.90],
            'split1_train_score' : [0.87, 0.82, 0.77, 0.84, 0.92],
            'mean_train_score'   : [0.86, 0.81, 0.76, 0.83, 0.91],
            'std_train_score'    : [0.01, 0.01, 0.01, 0.01, 0.01],
            'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49, 0.55],
            'std_fit_time'       : [0.01, 0.02, 0.01, 0.01, 0.02],
            'mean_score_time'    : [0.01, 0.06, 0.04, 0.04, 0.05],
            'std_score_time'     : [0.00, 0.00, 0.00, 0.01, 0.00],
            'params'             : [{'alpha': 1.0}, {'alpha': 10.0}, ...],
            }

        NOTE: The key ``'params'`` is used to store a list of parameter
        settings dicts for all the parameter candidates.

        The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
        ``std_score_time`` are all in seconds.

        For multi-metric evaluation, the scores for all the scorers are
        available in the ``cv_results_`` dict at the keys ending with that
        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
        above. (``'split0_test_mae'``, ``'mean_train_rmse'`` etc.)

    best_forecaster_ : BaseForecaster
        Forecaster that was chosen by the search, i.e. forecaster which gave
        highest score (or smallest loss if specified) on the left out data.
        Not available if ``refit=False``.

        See ``refit`` parameter for more information on allowed values.

    best_score_ : float
        Mean cross-validated score of the best_forecaster_.

        Follows sklearn's sign convention: for ``lower_is_better`` scorers
        (e.g. MAE, RMSE) the value is **negated** so that higher always
        means better.  The raw metric value is ``-best_score_``.

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

        This attribute is not available if ``refit`` is a function.

    best_params_ : dict
        Parameter setting that gave the best results on the hold out data.

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

    best_index_ : int
        The index (of the ``cv_results_`` arrays) which corresponds to the best
        candidate parameter setting.

        The dict at ``search.cv_results_['params'][search.best_index_]`` gives
        the parameter setting for the best model, that gives the highest
        mean score (``search.best_score_``).

        For multi-metric evaluation, this is present only if ``refit`` is
        specified.

    scorer_ : BaseScorer or dict
        Scorer function(s) used on the held out data to choose the best
        parameters for the model.

        For multi-metric evaluation, this attribute holds the validated
        ``scoring`` dict which maps the scorer key to the scorer callable.

    n_splits_ : int
        The number of cross-validation splits (folds/iterations).

    refit_time_ : float
        Seconds used for refitting the best forecaster on the whole dataset.

        This is present only if ``refit`` is not False.

    multimetric_ : bool
        Whether or not the scorers compute several metrics.

    n_features_in_ : int
        Number of features seen during ``fit``. Only defined if
        ``best_forecaster_`` is defined (see the documentation for the ``refit``
        parameter for more details) and that ``best_forecaster_`` exposes
        ``n_features_in_`` when fit.

    feature_names_in_ : ndarray of shape (n_features_in_,)
        Names of features seen during ``fit``. Only defined if
        ``best_forecaster_`` is defined (see the documentation for the ``refit``
        parameter for more details) and that ``best_forecaster_`` exposes
        ``feature_names_in_`` when fit.

    See Also
    --------
    - [`RandomizedSearchCV`][yohou.model_selection.search.RandomizedSearchCV] : Randomized search over parameter distributions.
    - [`ExpandingWindowSplitter`][yohou.model_selection.split.ExpandingWindowSplitter] : Cross-validation with expanding training windows.
    - [`SlidingWindowSplitter`][yohou.model_selection.split.SlidingWindowSplitter] : Cross-validation with sliding fixed-size windows.
    - [`MeanAbsoluteError`][yohou.metrics.point.MeanAbsoluteError] : Mean absolute error scorer.
    - [`RootMeanSquaredError`][yohou.metrics.point.RootMeanSquaredError] : Root mean squared error scorer.

    Notes
    -----
    The parameters selected are those that maximize the score of the left out
    data, unless an explicit scorer is passed in which case it is used instead.

    If n_jobs was set to a value higher than one, the data is copied for each
    point in the grid (and not n_jobs times). This is done for efficiency
    reasons if individual jobs take very little time, but may raise errors if
    the dataset is large and not enough memory is available.  A workaround in
    this case is to set ``pre_dispatch``. Then, the memory is copied only
    ``pre_dispatch`` many times. A reasonable value for ``pre_dispatch`` is
    ``2 * n_jobs``.

    Examples
    --------
    >>> from yohou.point import PointReductionForecaster
    >>> from yohou.model_selection import GridSearchCV
    >>> from yohou.metrics import MeanAbsoluteError
    >>> import polars as pl
    >>> from datetime import datetime, timedelta
    >>> # Create sample data
    >>> dates = [datetime(2020, 1, 1) + timedelta(days=i) for i in range(100)]
    >>> y = pl.DataFrame({"time": dates, "value": range(100)})
    >>> # Define parameter grid
    >>> param_grid = {
    ...     "estimator__alpha": [0.1, 1.0, 10.0],
    ...     "feature_transformer__lags": [[1], [1, 2]],
    ... }
    >>> # Single-metric search
    >>> search = GridSearchCV(
    ...     forecaster=PointReductionForecaster(),
    ...     param_grid=param_grid,
    ...     scoring=MeanAbsoluteError(),
    ...     cv=3,
    ... )
    >>> search.fit(y, forecasting_horizon=5)  # doctest: +SKIP
    >>> search.best_params_  # doctest: +SKIP
    >>> y_pred = search.predict(forecasting_horizon=5)  # doctest: +SKIP
    >>>
    >>> # Multi-metric search
    >>> from yohou.metrics import RootMeanSquaredError
    >>> scoring = {"mae": MeanAbsoluteError(), "rmse": RootMeanSquaredError()}
    >>> search = GridSearchCV(
    ...     forecaster=PointReductionForecaster(),
    ...     param_grid=param_grid,
    ...     scoring=scoring,
    ...     refit="mae",  # Use 'mae' to select best parameters
    ...     cv=3,
    ... )
    >>> search.fit(y, forecasting_horizon=5)  # doctest: +SKIP
    >>> search.best_params_  # doctest: +SKIP
    >>> # cv_results_ contains both 'mean_test_mae' and 'mean_test_rmse'
    >>> search.cv_results_["mean_test_mae"]  # doctest: +SKIP
    >>> search.cv_results_["mean_test_rmse"]  # doctest: +SKIP

    """

    _parameter_constraints: dict = {
        "param_grid": [dict, list],
    }

    def __init__(
        self,
        forecaster,
        param_grid,
        *,
        scoring=None,
        n_jobs=None,
        refit=True,
        cv=None,
        verbose=0,
        pre_dispatch="2*n_jobs",
        error_score=np.nan,
        return_train_score=False,
    ):
        super().__init__(
            forecaster=forecaster,
            scoring=scoring,
            n_jobs=n_jobs,
            refit=refit,
            cv=cv,
            verbose=verbose,
            pre_dispatch=pre_dispatch,
            error_score=error_score,
            return_train_score=return_train_score,
        )
        self.param_grid = param_grid

    def _run_search(self, evaluate_candidates):
        """Search all candidates in param_grid."""
        evaluate_candidates(ParameterGrid(self.param_grid))

Tutorials

The following example notebooks use this component:

• How to Tune Fourier Seasonality Terms

  ---

  Data-Features

  Explore how Fourier harmonic count affects seasonal fit quality, compare Fourier vs Pattern seasonality, and tune harmonics jointly with GridSearchCV.

  View · Open in marimo

• How to Create a Custom Scorer

  ---

  Evaluation-Search

  Implement a custom point scorer with aggregation, panel support, and systematic testing.

  View · Open in marimo

• How to Run Hyperparameter Search

  ---

  Evaluation-Search

  Tune forecaster hyperparameters with GridSearchCV and RandomizedSearchCV using temporal cross-validation splitters and result scatter visualisation.

  View · Open in marimo

• How to Search Interval Forecaster Hyperparameters

  ---

  Evaluation-Search

  Tune interval forecaster parameters directly with interval metrics in GridSearchCV, including mixed point+interval multimetric search.

  View · Open in marimo

• Forecasting Workflow

  ---

  Getting-Started

  Evaluate forecasters with cross-validation, search hyperparameters with GridSearchCV, and inspect residuals to diagnose model weaknesses.

  View · Open in marimo

• Reduction Forecasting Walkthrough

  ---

  Getting-Started

  Walk through the full fit/predict/evaluate cycle with PointReductionForecaster, cross-validation, and grid search on a real dataset.

  View · Open in marimo

• How to Run Panel Cross-Validation

  ---

  Panel-Data

  Time series cross-validation on panel data with GridSearchCV, selective group observation, rewind operations, and groupwise performance comparison.

  View · Open in marimo

• Quickstart

  ---

  Quickstart

  Comprehensive end-to-end tour of yohou beyond the Getting Started tutorials, covering data loading, baseline forecasting, preprocessing pipelines, decomposition, cross-validation search, and interval prediction.

  View · Open in marimo

• How to Visualize Model Selection Results

  ---

  Visualization

  Visualise CV fold geometry with expanding and sliding window splitters and hyperparameter search results with plot_splits and plot_cv_results_scatter.

  View · Open in marimo